Count Objects Crossing the Line¶

---

Before you start¶

Let's make sure that we have access to GPU. We can use nvidia-smi command to do that. In case of any problems navigate to Edit -> Notebook settings -> Hardware accelerator, set it to GPU, and then click Save.

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!nvidia-smi

!nvidia-smi

Fri Feb  9 17:05:30 2024       
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |
| N/A   45C    P8              10W /  70W |      0MiB / 15360MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+
                                                                                         
+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|  No running processes found                                                           |
+---------------------------------------------------------------------------------------+

In [ ]:

import os
HOME = os.getcwd()
print(HOME)

import os HOME = os.getcwd() print(HOME)

/content

Install required packages¶

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!pip install -q ultralytics supervision==0.18.0

!pip install -q ultralytics supervision==0.18.0

     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 709.5/709.5 kB 4.4 MB/s eta 0:00:00
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 86.7/86.7 kB 11.1 MB/s eta 0:00:00

Imports¶

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import numpy as np
import supervision as sv

from ultralytics import YOLO
from supervision.assets import download_assets, VideoAssets

import numpy as np import supervision as sv from ultralytics import YOLO from supervision.assets import download_assets, VideoAssets

Download video¶

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download_assets(VideoAssets.VEHICLES)

download_assets(VideoAssets.VEHICLES)

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SOURCE_VIDEO_PATH = f"{HOME}/vehicles.mp4"

SOURCE_VIDEO_PATH = f"{HOME}/vehicles.mp4"

Read single frame from video¶

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generator = sv.get_video_frames_generator(SOURCE_VIDEO_PATH)
frame = next(generator)

sv.plot_image(frame, (12, 12))

generator = sv.get_video_frames_generator(SOURCE_VIDEO_PATH) frame = next(generator) sv.plot_image(frame, (12, 12))

Run Object Detection¶

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model = YOLO("yolov8x.pt")

model = YOLO("yolov8x.pt")

Downloading https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x.pt to 'yolov8x.pt'...

100%|██████████| 131M/131M [00:00<00:00, 159MB/s]

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results = model(frame, verbose=False)[0]
detections = sv.Detections.from_ultralytics(results)

annotated_frame = sv.BoundingBoxAnnotator(thickness=4).annotate(frame.copy(), detections)
sv.plot_image(annotated_frame, (12, 12))

results = model(frame, verbose=False)[0] detections = sv.Detections.from_ultralytics(results) annotated_frame = sv.BoundingBoxAnnotator(thickness=4).annotate(frame.copy(), detections) sv.plot_image(annotated_frame, (12, 12))

Improve Vizualizations¶

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results = model(frame, verbose=False)[0]
detections = sv.Detections.from_ultralytics(results)
labels = [results.names[i] for i in detections.class_id]

h, w, _ = frame.shape

thickness = sv.calculate_dynamic_line_thickness(resolution_wh = (w, h))
text_scale = sv.calculate_dynamic_text_scale(resolution_wh = (w, h))

bounding_box_annotator = sv.BoundingBoxAnnotator(thickness=thickness)
label_annotator = sv.LabelAnnotator(text_thickness=thickness, text_scale=text_scale)

annotated_frame = frame.copy()
annotated_frame = bounding_box_annotator.annotate(annotated_frame, detections)
annotated_frame = label_annotator.annotate(annotated_frame, detections, labels)
sv.plot_image(annotated_frame, (12, 12))

results = model(frame, verbose=False)[0] detections = sv.Detections.from_ultralytics(results) labels = [results.names[i] for i in detections.class_id] h, w, _ = frame.shape thickness = sv.calculate_dynamic_line_thickness(resolution_wh = (w, h)) text_scale = sv.calculate_dynamic_text_scale(resolution_wh = (w, h)) bounding_box_annotator = sv.BoundingBoxAnnotator(thickness=thickness) label_annotator = sv.LabelAnnotator(text_thickness=thickness, text_scale=text_scale) annotated_frame = frame.copy() annotated_frame = bounding_box_annotator.annotate(annotated_frame, detections) annotated_frame = label_annotator.annotate(annotated_frame, detections, labels) sv.plot_image(annotated_frame, (12, 12))

Define Line Position¶

In [ ]:

sv.VideoInfo.from_video_path(SOURCE_VIDEO_PATH)

sv.VideoInfo.from_video_path(SOURCE_VIDEO_PATH)

Out[ ]:

VideoInfo(width=3840, height=2160, fps=25, total_frames=538)

In [ ]:

START = sv.Point(0, 2160 // 2)
END = sv.Point(3840, 2160 // 2)

line_zone = sv.LineZone(start=START, end=END)

line_zone_annotator = sv.LineZoneAnnotator(
    thickness=thickness,
    text_thickness=thickness,
    text_scale=text_scale)

annotated_frame = frame.copy()
annotated_frame = line_zone_annotator.annotate(annotated_frame, line_counter=line_zone)
sv.plot_image(annotated_frame, (12, 12))

START = sv.Point(0, 2160 // 2) END = sv.Point(3840, 2160 // 2) line_zone = sv.LineZone(start=START, end=END) line_zone_annotator = sv.LineZoneAnnotator( thickness=thickness, text_thickness=thickness, text_scale=text_scale) annotated_frame = frame.copy() annotated_frame = line_zone_annotator.annotate(annotated_frame, line_counter=line_zone) sv.plot_image(annotated_frame, (12, 12))

Process Video¶

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START = sv.Point(0, 1500)
END = sv.Point(3840, 1500)

line_zone = sv.LineZone(start=START, end=END)

line_zone_annotator = sv.LineZoneAnnotator(
    thickness=thickness,
    text_thickness=thickness,
    text_scale=text_scale)

START = sv.Point(0, 1500) END = sv.Point(3840, 1500) line_zone = sv.LineZone(start=START, end=END) line_zone_annotator = sv.LineZoneAnnotator( thickness=thickness, text_thickness=thickness, text_scale=text_scale)

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video_info = sv.VideoInfo.from_video_path(SOURCE_VIDEO_PATH)

video_info = sv.VideoInfo.from_video_path(SOURCE_VIDEO_PATH)

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thickness = sv.calculate_dynamic_line_thickness(resolution_wh=video_info.resolution_wh)
text_scale = sv.calculate_dynamic_text_scale(resolution_wh=video_info.resolution_wh)

bounding_box_annotator = sv.BoundingBoxAnnotator(thickness=thickness)
label_annotator = sv.LabelAnnotator(text_thickness=thickness, text_scale=text_scale)
trace_annotator = sv.TraceAnnotator(thickness=thickness)

thickness = sv.calculate_dynamic_line_thickness(resolution_wh=video_info.resolution_wh) text_scale = sv.calculate_dynamic_text_scale(resolution_wh=video_info.resolution_wh) bounding_box_annotator = sv.BoundingBoxAnnotator(thickness=thickness) label_annotator = sv.LabelAnnotator(text_thickness=thickness, text_scale=text_scale) trace_annotator = sv.TraceAnnotator(thickness=thickness)

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TARGET_VIDEO_PATH = f"{HOME}/vehicles-result.mp4"

TARGET_VIDEO_PATH = f"{HOME}/vehicles-result.mp4"

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byte_tracker = sv.ByteTrack(frame_rate=video_info.fps)

byte_tracker = sv.ByteTrack(frame_rate=video_info.fps)

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def callback(frame: np.ndarray, index:int) -> np.ndarray:
    results = model(frame, verbose=False)[0]
    detections = sv.Detections.from_ultralytics(results)
    detections = byte_tracker.update_with_detections(detections)

    labels = [
        f"#{tracker_id} {model.model.names[class_id]} {confidence:0.2f}"
        for confidence, class_id, tracker_id
        in zip(detections.confidence, detections.class_id, detections.tracker_id)
    ]

    annotated_frame = frame.copy()
    annotated_frame = trace_annotator.annotate(
        scene=annotated_frame,
        detections=detections)
    annotated_frame = bounding_box_annotator.annotate(
        scene=annotated_frame,
        detections=detections)
    annotated_frame = label_annotator.annotate(
        scene=annotated_frame,
        detections=detections,
        labels=labels)

    line_zone.trigger(detections)

    return  line_zone_annotator.annotate(annotated_frame, line_counter=line_zone)

def callback(frame: np.ndarray, index:int) -> np.ndarray: results = model(frame, verbose=False)[0] detections = sv.Detections.from_ultralytics(results) detections = byte_tracker.update_with_detections(detections) labels = [ f"#{tracker_id} {model.model.names[class_id]} {confidence:0.2f}" for confidence, class_id, tracker_id in zip(detections.confidence, detections.class_id, detections.tracker_id) ] annotated_frame = frame.copy() annotated_frame = trace_annotator.annotate( scene=annotated_frame, detections=detections) annotated_frame = bounding_box_annotator.annotate( scene=annotated_frame, detections=detections) annotated_frame = label_annotator.annotate( scene=annotated_frame, detections=detections, labels=labels) line_zone.trigger(detections) return line_zone_annotator.annotate(annotated_frame, line_counter=line_zone)

In [ ]:

sv.process_video(
    source_path = SOURCE_VIDEO_PATH,
    target_path = TARGET_VIDEO_PATH,
    callback=callback
)

sv.process_video( source_path = SOURCE_VIDEO_PATH, target_path = TARGET_VIDEO_PATH, callback=callback )